Big data and machine learning algorithms for health-care delivery.

By Kee Yuan Ngiam, Ing Wei Khor. The Lancet Oncology, 2019

1,049 citations recorded by the source corpus.

Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.

Subfields: Health Informatics, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/91fc647899f801c9d351349ce73779918f90a713

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Citations in the corpus

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “However, an elaboration of AI implementation barriers will be lengthy and is beyond the scope of this work, and we refer interested readers to the reports by Kelly et al [8], Ngiam and Khor [83], Lysaght et al [84], Shaw et al [10], and Yu and Kohane [12] for more details.”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, methodology): “Furthermore, major clinical oncological developments with the implementation of different AI and ML applications and algorithms include Chatbot; breast MRI interpretation; breast lesion classification; colorectal polyp classification; identification of colorectal cancer biomarkers in cell-free blood”
The ethics of AI in health care: A mapping review. (2020, background): “In this scenario, it remains unclear where the liability will eventually sit (Ngiam and Khor, 2019).”
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, methodology): “Furthermore, ML algorithms have the ability to analyze various data types (for instance, imaging data, demographic data, and laboratory ﬁndings) and integrate them into predictions for illness risk, diagnosis, prognosis, and applicable treatments [1].”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, methodology): “Large language models can be employed for tasks such as diagnosing diseases, recommending treatments, or analyzing patient data [121, 122].”
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023, neutral): “To facilitate adoption, the usability of the AI tool within the local clinical workflows should be evaluated in real world settings with representative and diverse end users (eg, with respect to sex, gender, age, clinical role, digital proficiency, and disability).”